Marco Dinarelli with his first journal publication in a IEEE review Marco Dinarelli
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LIG (UMR 5217)
Office 327
700 avenue Centrale
Campus de Saint-Martin-d’Hères, France

marco [dot] dinarelli [at] univ-grenoble-alpes [dot] fr
marco [dot] dinarelli [at] gmail [dot] com

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Latest news

2024 / 04 / 09:
Paper accepted at the French journal TAL, special issue on NLP models explainability

2024 / 02 / 20:
Paper accepted at the international conference LREC-COLING 2024

2024 / 02 / 04:
Paper accepted in the journal of Computer Speech and Language, Volume 84, Elsevier

Seq2Biseq - Bidirectional Output-wise Recurrent Neural Networks for Sequence Modelling

Content index:


Seq2Biseq tool is the software used for the paper Seq2Biseq: Bidirectional Output-wise Recurrent Neural Networks for Sequence Modelling. It replaces, extends and improves the previous tool LD-RNN, used for the paper Label-Dependencies Aware Recurrent Neural Networks.
Seq2Biseq is coded in pytorch and it follows the same research trend as our previous papers, where a bidirectional output-side context is used for current decision. A schema of the high-level architecture is shown in the following image.
Seq2Biseq model architecture

The idea is similar to those used in Deliberation Networks, and Asynchronous bidirectional networks for Machine Translation.


  • Bidirectional backward-forward decoding


Please send me an email @univ-grenoble-alpes.


Seq2Biseq is provided under Creative-Commons BY-SA licence

Installation and usage

See the README file in the package.